This is a special post for quick takes by Charlie Steiner. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.
Could someone who thinks capabilities benchmarks are safety work explain the basic idea to me?
It's not all that valuable for my personal work to know how good models are at ML tasks. Is it supposed to be valuable to legislators writing regulation? To SWAT teams calculating when to bust down the datacenter door and turn the power off? I'm not clear.
But it sure seems valuable to someone building an AI to do ML research, to have a benchmark that will tell you where you can improve.
But clearly other people think differently than me.
Not representative of motivations for all people for all types of evals, but https://www.openphilanthropy.org/rfp-llm-benchmarks/, https://www.lesswrong.com/posts/7qGxm2mgafEbtYHBf/survey-on-the-acceleration-risks-of-our-new-rfps-to-study, https://docs.google.com/document/d/1UwiHYIxgDFnl_ydeuUq0gYOqvzdbNiDpjZ39FEgUAuQ/edit, and some posts in seem relevant.
I think the core argument is "if you want to slow down, or somehow impose restrictions on AI research and deployment, you need some way of defining thresholds. Also, most policymaker's cruxes appear to be that AI will not be a big deal, but if they thought it was going to be a big deal they would totally want to regulate it much more. Therefore, having policy proposals that can use future eval results as a triggering mechanism is politically more feasible, and also, epistemically helpful since it allows people who do think it will be a big deal to establish a track record".
I find these arguments reasonably compelling, FWIW.
https://www.sciencedirect.com/science/article/abs/pii/S0896627321005018
(biorxiv https://www.biorxiv.org/content/10.1101/613141v2 )
Cool paper on trying to estimate how many parameters neurons have (h/t Samuel at EA Hotel). I don't feel like they did a good job distinguishing how hard it was for them to fit nonlinearities that would nonetheless be the same across different neurons, versus the number of parameters that were different from neuron to neuron. But just based on differences in physical arrangement of axons and dendrites, there's a lot of opportunity for diversity, and I do think the paper was convincing that neurons are sufficiently nonlinear that this structure is plausibly important. The question is how much neurons undergo selection based on this diversity, or even update their patterns as a form of learning!